Driving Reaction Trajectories via Latent Flow Matching

• URL: http://arxiv.org/abs/2602.10476v1
• Date: Wed, 11 Feb 2026 03:28:20 GMT
• Title: Driving Reaction Trajectories via Latent Flow Matching
• Abstract summary: LatentRxnFlow is a new reaction prediction paradigm that models reactions as continuous latent trajectories anchored at the thermodynamic product state.<n>LatentRxnFlow achieves strong accuracy with improved transparency, diagnosability, and uncertainty awareness.
• Score: 21.71509185571088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in reaction prediction have achieved near-saturated accuracy on standard benchmarks (e.g., USPTO), yet most state-of-the-art models formulate the task as a one-shot mapping from reactants to products, offering limited insight into the underlying reaction process. Procedural alternatives introduce stepwise generation but often rely on mechanism-specific supervision, discrete symbolic edits, and computationally expensive inference. In this work, we propose LatentRxnFlow, a new reaction prediction paradigm that models reactions as continuous latent trajectories anchored at the thermodynamic product state. Built on Conditional Flow Matching, our approach learns time-dependent latent dynamics directly from standard reactant-product pairs, without requiring mechanistic annotations or curated intermediate labels. While LatentRxnFlow achieves state-of-the-art performance on USPTO benchmarks, more importantly, the continuous formulation exposes the full generative trajectory, enabling trajectory-level diagnostics that are difficult to realize with discrete or one-shot models. We show that latent trajectory analysis allows us to localize and characterize failure modes and to mitigate certain errors via gated inference. Furthermore, geometric properties of the learned trajectories provide an intrinsic signal of epistemic uncertainty, helping prioritize reliably predictable reaction outcomes and flag ambiguous cases for additional validation. Overall, LatentRxnFlow combines strong predictive accuracy with improved transparency, diagnosability, and uncertainty awareness, moving reaction prediction toward more trustworthy deployment in high-throughput discovery workflows.

Related papers

• Adaptive Recurrent Message Passing for Test Time Computing on Graphs [80.56829418752513]
  We show that recurrent graph models can overcome the intrinsic mismatch between graph data and architectural designs.<n>We propose AdaR, an Adaptive Recurrent graph model, empowering flexible test-time computing on various downstream tasks without changing model parameters.
  arXiv  Detail & Related papers  (2026-06-21T12:14:11Z)
• Neural Bayesian Sequential Routing [0.0]
  We introduce textbfNeural Bayesian Sequential Routing (NBSR), a framework that models neural inference as active evidence accumulation over a hierarchical Directed Acyclic Graph (DAG)<n>NBSR achieves competitive predictive performance while providing transparent routing traces, path-dependent evidence attribution, uncertainty-aware decision control, and resource-rational inference.<n>Overall, NBSR offers a mathematically grounded framework for interpretable, modular, and resource-rational agentic AI.
  arXiv  Detail & Related papers  (2026-05-22T22:45:56Z)
• Probability-Conserving Flow Guidance [49.03107678763765]
  Adaptive Manifold Guidance (AdaMaG) improves realism, reduces hallucinations, and controlled desaturation in high-guidance.<n>Across image generation benchmarks, AdaMaG improves realism, reduces hallucinations, and controlled desaturation in high-guidance.
  arXiv  Detail & Related papers  (2026-05-19T16:34:01Z)
• Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics [42.27928681606673]
  Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning.<n>We investigate the hidden representations of LRMs to determine whether future behavior can be predicted from prompt and CoT representations.<n>We find that future model behavior is more distinguishable when examined over the full trajectory than from a single static prediction.
  arXiv  Detail & Related papers  (2026-05-18T15:29:04Z)
• Rare Event Analysis via Stochastic Optimal Control [39.211234628277744]
  We introduce a framework that casts committor estimation as an optimal control (SOC) problem.<n>We develop two complementary objectives: a direct backpropagation loss and a principled off-policy Value Matching loss.<n>On benchmark systems, the framework yields more accurate committor estimates, reaction rates, and equilibrium constants than existing methods.
  arXiv  Detail & Related papers  (2026-04-14T18:34:38Z)
• Diverging Flows: Detecting Extrapolations in Conditional Generation [3.1784840992666137]
  Diverging Flows is a novel approach that enables a single model to simultaneously perform conditional generation and native extrapolation detection.<n>It achieves effective detection of extrapolations without compromising predictive fidelity or inference latency.<n>These results establish Diverging Flows as a robust solution for trustworthy flow models, paving the way for reliable deployment in domains such as medicine, robotics, and climate science.
  arXiv  Detail & Related papers  (2026-02-13T16:15:58Z)
• Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting [0.0]
  We propose a hybrid architecture that injects a geostatistic inductive bias directly into the decomposing self-attention mechanism via a learnable costatistics kernel.<n>We demonstrate the phenomenon of Deep Variography'', where the network successfully recovers the true spatial parameters of the underlying process end-to-end via backpropagation.
  arXiv  Detail & Related papers  (2025-12-19T15:32:24Z)
• Efficient Thought Space Exploration through Strategic Intervention [54.35208611253168]
  We propose a novel Hint-Practice Reasoning (HPR) framework that operationalizes this insight through two synergistic components.<n>The framework's core innovation lies in Distributional Inconsistency Reduction (DIR), which dynamically identifies intervention points.<n> Experiments across arithmetic and commonsense reasoning benchmarks demonstrate HPR's state-of-the-art efficiency-accuracy tradeoffs.
  arXiv  Detail & Related papers  (2025-11-13T07:26:01Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Adaptive Conformal Prediction Intervals Over Trajectory Ensembles [50.31074512684758]
  Future trajectories play an important role across domains such as autonomous driving, hurricane forecasting, and epidemic modeling.<n>We propose a unified framework based on conformal prediction that transforms sampled trajectories into calibrated prediction intervals with theoretical coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-18T21:14:07Z)
• RAAG: Ratio Aware Adaptive Guidance [9.525432706814675]
  Flow-based generative models have achieved remarkable progress.<n>Applying a strong, fixed guidance scale throughout inference is poorly suited for the rapid, few-step sampling required by modern applications.<n>We propose a simple, theoretically grounded, adaptive guidance schedule that automatically dampens the guidance scale at early steps based on the evolving ratio.
  arXiv  Detail & Related papers  (2025-08-05T13:41:05Z)
• Electron flow matching for generative reaction mechanism prediction obeying conservation laws [8.136277960071032]
  This work recasts the problem of reaction prediction as a problem of electron redistribution using the modern deep generative framework of flow matching.<n>Our model, FlowER, overcomes limitations by enforcing exact mass conservation, thereby resolving hallucinatory failure modes.<n>FlowER additionally enables estimation of thermodynamic or kinetic feasibility and manifests a degree of chemical intuition in reaction prediction tasks.
  arXiv  Detail & Related papers  (2025-02-18T16:01:17Z)
• FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation [15.64941169350615]
  Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems.<n>FlowDAS is a generative DA framework that uses interpolants to learn state transition dynamics.<n>We show that FlowDAS surpasses model-driven methods, neural operators, and score-based baselines in accuracy and physical plausibility.
  arXiv  Detail & Related papers  (2025-01-13T05:03:41Z)
• Selective Learning: Towards Robust Calibration with Dynamic Regularization [79.92633587914659]
  Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. We introduce Dynamic Regularization (DReg) which aims to learn what should be learned during training thereby circumventing the confidence adjusting trade-off.
  arXiv  Detail & Related papers  (2024-02-13T11:25:20Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.